Zero-shot Adaptation. We present additional experiments on the Deep Mind Control suite [1]} in the Appendix. As in Plan2Explore [2]}, we use intrinsic motivation to train an exploration policy, which collects data to improve the agent's model. Then, the model is used to train an exploitative policy on the environment's rewards and its zero-shot performance is evaluated. In these visual control tasks, we show that the intrinsic motivation bonus of LBS combines well with model-based RL, achieving similar or higher performance than Plan2Explore and requiring no additional predictors to be trained.
